I have made a function for training data which pick random files either positive or negative. it is a binary classification problem.
model=Sequential()
InputBatch = np.expand_dims(InputBatch, 0)
print(InputBatch.shape)
model.add(LSTM(100,input_shape=(1,6,30),return_sequences=True))
model.compile(loss='mean_absolute_error',optimizer='adam',metrics=['accuracy'])
model.fit(InputBatch,PositiveOrNegativeLabel,batch_size=6,nb_epoch=10,verbose=1,validation_split=0.05)
the shape of InputBatch variable is (1,6,30)
for example my input data is
[[ nan 1520. 1295. nan 8396. 9322. 12715. nan 5172. 7232.
11266. nan 11266. 2757. 4416. 12020. 12111. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ nan 3045. 11480. 900. 5842. 11496. 4463. nan 11956. 900.
10400. 8022. 2504. 12106. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ nan 9307. 12003. 2879. 6398. 9372. 4614. 5222. nan nan
2879. 10364. 6923. 4709. 4860. 11871. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ nan 6689. 2818. 12003. 6480. nan 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ nan 3395. 1087. 11904. 7232. 8840. 10115. 4494. 11516. 7441.
8535. 12106. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ nan 1287. 420. 4070. 11087. 7410. 12186. 2387. 12111. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
I have set the shape of data which is (6,30)
I am getting value error
ValueError: Error when checking input: expected lstm_16_input to have 3 dimensions, but got array with shape (1,6, 30)
it is getting three dimension input i don't understand how and why
LSTM inputs have to be in 3 dimensional(samples,timesteps,features). And your data seems to be in 2D. You can use the numpys reshape() function to convert your data to 3D.
For example if you are using 1 time-step then you will have to reshape it as array.reshape(6,1,30) or if you are using 6 timestep then array.reshape(1,6,30)
For more information on reshaping input for LSTM you can check this site
[[update]] There are so many problems with your code
model=Sequential()
InputBatch = np.expand_dims(InputBatch, 0)
print(InputBatch.shape)
model.add(LSTM(100,input_shape=(1,6,30),return_sequences=True))
model.compile(loss='mean_absolute_error',optimizer='adam',metrics=['accuracy'])
model.fit(InputBatch,PositiveOrNegativeLabel,batch_size=6,nb_epoch=10,verbose=1,validation_split=0.05)
When you convert your data to (1,6,30) you are basically saying that you have just one sample(only 1) ,batch_size is 6 but you have only 1 sample,you have only one sample but you are doing validation split.And since you have only one X value it will have only one Y ( PositiveOrNegativeLabel),So I have asisgneed just one value ie 1.
I have ran your program with the changes few changes with code and data that u have shown in the question (i changed NA to 0) :
a=np.array([
[0,1520,1295,0,8396,9322,12715,0,5172,7232,11266,0,11266,2757,4416,12020,12111,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,3045,11480,900,5842,11496,4463,0,11956,900,10400,8022,2504,12106,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,9307,12003,2879,6398,9372,4614,5222,0,0,2879,10364,6923,4709,4860,11871,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,6689,2818,12003,6480,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,3395,1087,11904,7232,8840,10115,4494,11516,7441,8535,12106,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,1287,420,4070,11087,7410,12186,2387,12111,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
]
)
PositiveOrNegativeLabel=np.array([[1]])
PositiveOrNegativeLabel=PositiveOrNegativeLabel.reshape(1,-1)
PositiveOrNegativeLabel.shape
InputBatch =InputBatch.reshape(1,6,30)
InputBatch.shape
model=Sequential()
model.add(LSTM(1,input_shape=(6,30)))
model.add(Dense(1,activation="sigmoid"))
model.compile(loss='mean_absolute_error',optimizer='adam',metrics=['accuracy'])
model.fit(InputBatch,PositiveOrNegativeLabel,batch_size=1,verbose=1)